Typically, communications between various nodes on a network are transmitted through a dedicated medium such as coaxial cable, twisted pair wire, or fiber optic cable. These media can sometimes be expensive as they have to meet stringent transmission criteria. In addition, significant costs are incurred for physically routing the medium to each of the various nodes. Hence, it would be beneficial if the nodes could take advantage of an already existing medium for communication purposes.
One such medium is power distribution lines such as those found in virtually all homes, offices, and factories. In many cases these power lines distribute 120 volt AC (alternating current) to wall sockets, thereby supplying power to various devices such as appliances, computers, lights, etc. Because power lines are designed primarily for transmitting power, they are not ideally suited for communications. One major problem is that noise on power lines can be quite high due to electrical interference emanating from the very devices being powered.
Noise can have many of the same characteristics as the data being transmitted. Conversely, data signals can be severely distorted by the power line. Thus, a received signal will generally contain both distorted data signals and additive noise. Therefore, some mechanism must be implemented to distinguish distorted data plus noise from noise only in order to maximize the output signal to noise ratio (SNR).
This mechanism is called signal or carrier detection. Carrier detection can be based on the received signal energy level or on the level of correlation to an expected reference. In either case, the carrier detector typically processes the received signal to generate a qualification signal. This qualification signal is then compared to a threshold to determine how likely it is that the incoming signal contains a valid message. The goal is to fix a threshold such that random noise does not trigger the signal detect indication, while valid signals of less than ideal energy or correlation do trigger signal detect indications.
In a shared-access network, each transceiver typically has a channel busy indicator, which is used to avoid collisions by holding off new transmissions when the channel has been determined to be in use. Consequently, false signal detections, which initiate a channel busy indication, reduce the overall network throughput. Thus network throughput is maximized by selecting a threshold high enough so that noise does not exceed it. On the other hand, a transceiver's sensitivity to weak or distorted messages is maximized by selecting a lower threshold. Thus, a threshold must be selected which optimize system performance considering these competing criteria.
Further complicating matters is the fact that the optimum threshold can vary with time as the network environment changes. Selecting a fixed threshold is therefore a poor compromise, and an adaptive threshold mechanism is needed. Although it is apparent which parameter needs to be adapted (i.e., the signal detect threshold) determining an optimal adaptation algorithm is less obvious.
One prior art approach for detecting valid signals involves correlating a received signal against a known, expected waveform. When using a correlator for a signal detect function, it is desired to know the peak of the cross correlation between a received waveform and an expected waveform. The peak of the cross correlation is the value which shows how closely the received waveform matches the reference (expected) waveform. There are basically two kinds of correlators known in the art--parallel correlators and serial correlators. Parallel correlators perform an integration over a given time interval (e.g., usually the period of the reference waveform) of the product of two waveforms. This integration is performed at a rate of every sample time. Hence, the peak of the correlation can then be determined to within the accuracy of the sample time. The integration of the product of the two waveforms has to be done in real time. Parallel correlators require a great deal of hardware because of all the numerous multiplication and addition functions that are required (per unit time). Also, both waveforms have to be stored so that, at every sample time (there are usually many sample times during a waveform), the full time duration of both waveforms can be multiplied and integrated. This requires additional hardware to store the waveforms. Consequently, parallel correlators are quite complex and expensive to implement.
Serial correlators, on the other hand, perform one integration over some time interval (usually the period of the reference waveform) of the product of two waveforms, but only at a rate commensurate with the period of the reference waveform. Therefore, only a single multiply and accumulate needs to be performed at every sample time. Consequently, the amount of hardware required is small. Furthermore, only one sample of each waveform needs to be in storage at a given sample time. A limitation of serial correlators is that they only find the value of correlation at one phase. The correlation value varies with phase so that the phase of the peak correlation must first be found. For a modulated signal, the peak correlation varies with both carrier phase and modulated waveform phase. Thus, prior to performing the serial integration, the peak over all the phases must be found. At each phase and for the period of the reference waveform, a serial waveform multiply and integrate is performed. The problem with this approach is that it takes significant time to detect a valid signal.
It is critical to keep the signal detect time to a minimum because some communication systems are designed to transmit a large number of small messages (e.g., packets). The throughput of such systems would be severely limited if it took a long time to detect each of the signals associated with these small, but numerous, messages. The problem, however, is that the shorter the amount of time allowed to detect the signal, the harder it becomes to accurately detect it. In other words, as the detect time is decreased, the probability for false signal detects increases.
Further complicating matters is that in some environments, there may be interfering tones imposed on the communication channel. Switch-mode power supplies, for example, conduct interference back onto power distribution lines at their operating frequency and its harmonics. In the event that one of these interfering tones occurs at a frequency near the communication frequency, it might be falsely interpreted as a valid transmission. Hence, it would be beneficial if there were some means to further distinguish valid signals from interfering tones.
Another challenge in signal detection is related to the transmission of different types of data packets on the same medium. Different types of data packets being transmitted over the same medium at the same time may interfere with each other. In order to address this issue, in some cases, an access protocol is promulgated by a governing body to regulate the transmissions of various modems/transceivers manufactured by different vendors. Such an access protocol specifies certain transmission characteristics and signal detection characteristics that must be met so that the medium can be efficiently and harmoniously shared by different types of transceivers.
Therefore, what is needed is an apparatus and method for reliably and accurately detecting a valid signal in a communications system. It would be preferable for the apparatus and method to adaptively optimize its detection criteria to changes in the operating environment. Furthermore, it would be beneficial if such an apparatus and method minimized the hardware requirements and the carrier detect time by implementing a correlator which uses serial correlations and does not have to take time to search for the peak phase. It would also be preferable if such an apparatus and method maximized its performance without violating any applicable access protocol.